@inproceedings{Resnick:1994:GOA:192844.192905,
 author = {Resnick, Paul and Iacovou, Neophytos and Suchak, Mitesh and Bergstrom, Peter and Riedl, John},
 title = {GroupLens: an open architecture for collaborative filtering of netnews},
 booktitle = {Proceedings of the 1994 ACM conference on Computer supported cooperative work},
 series = {CSCW '94},
 year = {1994},
 isbn = {0-89791-689-1},
 location = {Chapel Hill, North Carolina, United States},
 pages = {175--186},
 numpages = {12},
 url = {http://doi.acm.org/10.1145/192844.192905},
 doi = {http://doi.acm.org/10.1145/192844.192905},
 acmid = {192905},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Usenet, collaborative filtering, electronic bulletin boards, information filtering, netnews, selective dissemination of information, social filtering, user model},
} 

@INPROCEEDINGS{Schein02methodsand,
    author = {Andrew I. Schein and Alexandrin Popescul and Lyle H. and Rin Popescul and Lyle H. Ungar and David M. Pennock},
    title = {Methods and Metrics for Cold-Start Recommendations},
    booktitle = {In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
    year = {2002},
    pages = {253--260},
    publisher = {ACM Press}
}

@INPROCEEDINGS{Breese98empiricalanalysis,
    author = {John S. Breese and David Heckerman and Carl Kadie},
    title = {Empirical Analysis of Predictive Algorithms for Collaborative Filtering},
    booktitle = {},
    year = {1998},
    pages = {43--52},
    publisher = {Morgan Kaufmann}
}

@ INPROCEEDINGS{Tang_uncoveringgroups,
    author = {Lei Tang and Xufei Wang and Huan Liu},
    title = {Uncovering Groups via Heterogeneous Interaction Analysis},
    booktitle = {ICDM},
    year = {2009}
}

@article{Fortunato201075,
title = "Community detection in graphs",
journal = "Physics Reports",
volume = "486",
number = "3-5",
pages = "75 - 174",
year = "2010",
note = "",
issn = "0370-1573",
doi = "DOI: 10.1016/j.physrep.2009.11.002",
url = "http://www.sciencedirect.com/science/article/B6TVP-4XPYXF1-1/2/99061fac6435db4343b2374d26e64ac1",
author = "Santo Fortunato",
keywords = "Graphs",
keywords = "Clusters",
keywords = "Statistical physics"
}


@article{Delong_Erickson_2008,
 title={Social Topic Models for Community Extraction Categories and Subject Descriptors}, url={http://www-users.cs.umn.edu/~banerjee/papers/08/snakdd08.pdf}, abstract={With social interaction playing an increasingly important role in the online world, the capability to extract latent com- munities based on such interactions is becoming vital for a wide variety of applications. However, existing literature on community extraction has largely focused on methods based on the link structure of a given social network. Such link-based methods ignore the content of social interactions, which may be crucial for accurate and meaningful commu- nity extraction. In this paper, we present a Bayesian genera- tive model for community extraction which naturally incor- porates both the link and content information present in the social network. The model assumes that actors in a com- munity communicate on topics of mutual interest, and the topics of communication, in turn, determine the communi- ties. Further, the model naturally allows actors to belong to multiple communities. The model is instantiated in the con- text of an email network, and a Gibbs sampling algorithm is presented to do inference. Through extensive experiments and visualization on the Enron email corpus, we demon- strate that the model is able to extract well-connected and topically meaningful communities. Additionally, the model extracts relevant topics that can be mapped back to corre- sponding real-life events involving Enron}, journal={October}, author={Delong, Colin and Erickson, Kendrick}, year={2008}}
